随着光伏发电系统的广泛应用，直流串联故障电弧已成为威胁光伏系统安全运行的因素之一，为了解决光伏串联故障电弧难以准确识别的问题，该文使用不同形状电极，开展不同电流强度下的光伏直流串联故障电弧实验，获取了故障电弧特征，构建了不同电极形状条件下的伏安特性曲线，然后结合数学统计和快速Fourier变换(FFT)方法对故障信号的时域、频域特征进行提取与分析。研究发现光伏直流串联故障电弧电流信号在0~50 kHz频段的特征较为明显，且不同形状电极产生的故障电弧频谱能量差异明显。最后，基于此特征构建了FFT-1DCNN-LSTM模型，通过模型训练，实现了不同形状电极产生故障电弧的准确识别，准确率达到99.87%，可为光伏系统故障电弧检测提供有效技术手段。

Objective: Given the increasing deployment of photovoltaic (PV) power generation systems worldwide, ensuring the safe and reliable operation of these systems is of paramount importance. Among the various safety concerns, DC series arc faults have emerged as a significant threat to the stability and performance of PV systems. These faults, often the result of disconnections or substandard connections within the PV circuit, can potentially result in fires and system failures if not identified promptly. The objective of this study is to accurately identify DC series arc faults in PV systems, which would greatly enhance the safety measures and operational reliability of these systems. In addition, the study investigates the characteristics of these faults under different experimental conditions, focusing on the impact of electrode shapes and current intensity on fault characteristics. Finally, a robust model for detecting such faults is developed based on extracted time-domain and frequency-domain features of fault signals. Methods: A series of experiments were conducted to simulate DC series arc faults in PV systems. The experiments utilized electrodes of various shapes to study the effect of electrode design on arc characteristics and were performed under various current intensities to simulate real-world operating conditions of PV systems. For each fault condition, the voltage-current characteristics of the arcs were recorded to gain an initial understanding of the fault dynamics. The data were then analyzed using both time-domain and frequency-domain methods. Specifically, the Fast Fourier Transform (FFT) was used to convert time-domain fault signals into the frequency domain for further feature extraction. Mathematical statistics techniques were also applied to analyze the spectral energy distribution across different frequency bands, with a particular focus on the 0—50 kHz frequency range, which has been identified as critical for distinguishing different arc fault signatures. Based on these extracted features, a hybrid model combining FFT and deep learning techniques was developed. This model integrates the FFT with a 1D Convolutional Neural Network (1DCNN) and a Long Short-Term Memory (LSTM) network. This architecture identifies arc fault types based on their frequency-domain characteristics. Results: The experimental results revealed that the frequency characteristics of DC series arc faults highly depend on the shape of the electrodes and the current intensity. Specifically, faults generated by electrodes of different shapes exhibited distinct features in the frequency domain, with significant variations observed in the spectral energy distribution within the 0—50 kHz frequency range. These results imply that electrode shape plays a significant role in determining the frequency signature of arc faults, which can be used for fault identification purposes. The FFT-based feature extraction technique successfully isolated the most relevant frequency components indicative of arc faults. The FFT-1DCNN-LSTM model was then trained using these features and achieved an accuracy rate of 99.87% in correctly classifying arc faults generated by different electrode shapes. This result demonstrates the model's robustness and potential for real-world applications, as it can effectively differentiate various fault scenarios in PV systems. Furthermore, the model's high accuracy indicates its potential for the early detection of arc faults, which can significantly improve the safety and reliability of PV systems. Conclusions: This study introduces an effective and innovative method for detecting DC series arc faults in PV systems. By analyzing fault characteristics under different electrode shapes and current intensities, the study provides valuable insights into the role of these parameters in detection. The FFT-based feature extraction method, combined with an advanced deep learning model (FFT-1DCNN-LSTM), achieves exceptional performance in accurately identifying arc faults. The model's high classification accuracy highlights its potential for practical deployment in real PV systems, where early arc fault detection is critical for preventing potential hazards such as fires. The study's findings contribute to ongoing efforts to enhance system safety and provide a reliable technical framework for arc fault detection and mitigation. Future research may focus on refining the model by including additional fault scenarios and exploring its scalability to larger, more complex PV system configurations.